TY - JOUR
T1 - Separation of composite defect patterns on wafer bin map using support vector clustering
AU - Wang, Chih-Hsuan
PY - 2009/3
Y1 - 2009/3
N2 - By continuously employing new technologies, huge capital investment, and well-trained engineers, integrated circuit (IC) companies compete intensively with each other to enhance product yield, reduce manufacturing cost and increase profitability. However, wafer fabrication is a complex, costly, and lengthy process that involves hundreds of chemical steps and needs to monitor lots of process parameters at the same time. As we know, IC chips fabricated on semiconductor wafers are highly vulnerable to clustered defects since defects may cause IC to completely malfunction. In particular, various types of defect patterns (i.e. scratch, ring, or zone pattern) shown on the wafer bin map (WBM) usually contain crucial information for quality engineers to track their root causes of failure. Today, many companies still rely on the visual inspection of experienced experts to check and to hand-mark the defective regions on WBM. This manual approach is not only time-consuming, but also inefficient and inconsistent owing to human fatigue. In this study, a hybrid approach that integrates spatial statistics, kernel based eigen-decomposition and support vector clustering is proposed to estimate the number of defect clusters in advance, and to separate both convex and non-convex defect clusters at the same time. Experimental results confirm that four kinds of composite defect patterns are successfully extracted and separated.
AB - By continuously employing new technologies, huge capital investment, and well-trained engineers, integrated circuit (IC) companies compete intensively with each other to enhance product yield, reduce manufacturing cost and increase profitability. However, wafer fabrication is a complex, costly, and lengthy process that involves hundreds of chemical steps and needs to monitor lots of process parameters at the same time. As we know, IC chips fabricated on semiconductor wafers are highly vulnerable to clustered defects since defects may cause IC to completely malfunction. In particular, various types of defect patterns (i.e. scratch, ring, or zone pattern) shown on the wafer bin map (WBM) usually contain crucial information for quality engineers to track their root causes of failure. Today, many companies still rely on the visual inspection of experienced experts to check and to hand-mark the defective regions on WBM. This manual approach is not only time-consuming, but also inefficient and inconsistent owing to human fatigue. In this study, a hybrid approach that integrates spatial statistics, kernel based eigen-decomposition and support vector clustering is proposed to estimate the number of defect clusters in advance, and to separate both convex and non-convex defect clusters at the same time. Experimental results confirm that four kinds of composite defect patterns are successfully extracted and separated.
KW - Data mining
KW - Defect pattern
KW - Kernel decomposition
KW - Support vector clustering
KW - Wafer binary map
UR - http://www.scopus.com/inward/record.url?scp=56649124420&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2008.01.057
DO - 10.1016/j.eswa.2008.01.057
M3 - Article
AN - SCOPUS:56649124420
VL - 36
SP - 2554
EP - 2561
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
IS - 2 PART 1
ER -